Bibliographic record
Abstract
Development planners and project managers have used a wide variety of tools to manage a broad range of environmental risks, including those posed by climate variability, for a long time. Some of these tools have also now been modified to take into account the risks posed by climate change. At the same time, there has been a recent emphasis in developing more dedicated tools which have an explicit focus on screening for climate change risks and for facilitating adaptation. The purpose of this paper is to analyse this latter set of tools targeted to screen climate change risks. The paper focuses on the need to consider the experiences of users as well as developers, and to investigate the extent to which tools are meeting user needs and if opportunities may exist for streamlining the tools landscape. This analysis is therefore an effort to contribute to the alignment and harmonisation priorities of the Paris Declaration on Aid Effectiveness of March 2005 and the follow-up Accra Agenda for Action of September 2008. While a “one-size-fits-all” approach or methodology may not be appropriate, there may be opportunities to provide common guidance on specific topics, such as categorisation and risk management frameworks, and to clarify the diverse terminology. In an effort to improve the use of screening and assessment tools, the paper recommends that the development community increase partner country ownership of risk screening and assessment tools/processes, narrow the gap between process guidance tools and data and information provision tools, supply guidance for users in moving from analysis to action and collaborate to prepare harmonised guidelines. While this analysis is limited to tools which have an explicit focus on climate change and adaptation, future work should also consider existing risk analysis tools which are practically used in development planning and modified for applications to adaptation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".